Sales & Operational Planning Analyst

Just Food For Dogs•Remote, US,

17h•$70,000 - $80,000•Remote

About The Position

This is not a traditional planning role. We have built — and continue to evolve — an automated, statistical forecasting system that generates an unconstrained demand forecast at the sellable SKU × customer level, layers in commercial and sales team inputs, and produces a Microsoft Fabric-ready import file each S&OP cycle. The engine runs a model tournament per SKU×customer combination, integrates promotional lift, new distribution, innovation cannibalization, and post-meeting consensus adjustments, and outputs forecast accuracy analytics alongside safety stock recommendations. We are looking for a Sales & Operational Planning Analyst who is eager to learn, analytically curious, and excited about working at the intersection of data, commercial planning, and supply chain. Reporting to the Sales and Operational Planning Manager, this role is focused on the execution and continuous improvement of the demand planning process — running the engine each cycle, coordinating inputs from commercial and sales teams, maintaining data quality, and helping identify where the forecast can get sharper. You will work closely you’re your Manager, and cross-functional teams to build a planning process that is reliable, transparent, and genuinely useful to the business.

Requirements

1–3 years of experience in demand planning, supply chain analytics, or a closely related commercial or operations analytics role — we are looking for high-potential candidates, not necessarily those with an extensive track record
Analytical curiosity and comfort working with data: you enjoy digging into numbers, asking why, and figuring out what the data is actually saying
Proficiency in Excel is a must and working familiarity with Python or SQL is a plus— you don't need to be a software engineer, but you should be comfortable running scripts, reading outputs, and doing basic data manipulation
Ability to manage a repeatable monthly process with multiple stakeholders and deadlines — organized, reliable, and proactive when things go off-track
Clear written and verbal communication skills, including the ability to explain a complex forecast or data issue in plain language to a non-technical audience
Genuine interest in understanding how forecasting models work and how to make them better — not just accepting the output, but questioning it
Experience with Microsoft Fabric, Power BI, or similar BI/planning platforms is a plus but not required; willingness and ability to learn new tools quickly is essential
NetSuite ERP familiarity is a plus; broader ERP experience in a manufacturing or consumer goods environment is beneficial
Bachelor's degree in Supply Chain, Statistics, Business Analytics, or a related field preferred
Genuine passion for the JustFoodForDogs mission and helping pets live longer, healthier lives

Nice To Haves

Exposure to statistical or machine learning forecasting methods — even at the academic or self-taught level — and an interest in applying them to real business problems
Familiarity with multi-channel distribution: direct-to-consumer digital (Amazon, Chewy, web), wholesale retail accounts, and company-operated stores
Understanding of sell-in vs. sell-through demand signals and why that distinction matters for major retail and digital accounts
Experience in fresh, frozen, or perishable food with shelf-life and inventory constraints

Responsibilities

Run the monthly statistical forecast engine (Python-based) against the latest NetSuite sellable-unit actuals export; validate outputs, investigate anomalies, and ensure data quality before the demand review meeting
Coordinate template inputs from commercial teams (promotional lift in units or gross/net $), sales teams (new distribution wins), and commercial/innovation teams (new product launch forecasts and cannibalization estimates) — on time, every cycle
Prepare the layered forecast (Base → Promo → New Distribution → Innovation/Cannibalization → Final) at the channel and category level for the S&OP Manager to present and facilitate in the demand review meeting; participate in the meeting and support the discussion
Capture post-meeting consensus adjustments with reason codes, apply them through the override template, run the final integration, and import the Fabric file — closing the loop each cycle cleanly
Maintain the CONFIG section of the engine each month: update the actuals file reference, last actual period, and template inputs as the business evolves
Monitor and report weighted MAPE (wMAPE) at the business, channel, and customer level — not just a headline number, but a diagnostic that drives action
Run monthly forecast accuracy analysis comparing the prior-period demand forecast against actualized results; prepare accuracy and bias by channel, category, and top SKUs for review with the S&OP Manager and executive leadership
Investigate accuracy outliers: distinguish genuine model misses from data gaps (e.g., variety pack SKUs with no sellable-unit history, new distribution ramps not yet in actuals)
Use the model tournament results (Naive, SES, Holt's, LightGBM) to identify which SKU×customer combinations need better signal — and whether the right fix is more data, a different model, or a sell-through feed replacing sell-in
Maintain the sku_lead_times.csv with supply team input; ensure safety stock recommendations reflect current production lead times rather than defaulting to the fallback
Work alongside the S&OP Manager to facilitate collaboration with commercial and sales teams on monthly template completion — helping teams understand what inputs are needed, by when, and why they matter to the forecast
Support onboarding of commercial team members to the process alongside the S&OP Manager: what 'incremental lift above base' means, how gross revenue entries get converted to units, and why the base model cannot see events it hasn't been told about
Surface significant forecast variances and data issues to the S&OP Manager for discussion with commercial teams — with specific, actionable context rather than just a percentage
Work alongside the S&OP Manager to maintain the relationship between the forecasting system and the NetSuite/Fabric data pipeline: validate each actuals export, flag SKU ID changes or format shifts before they break the engine, and help coordinate the inclusion of new pack SKUs in the sellable-unit export
Support the sell-through integration roadmap alongside the S&OP Manager: work with Amazon (Vendor Central Sales Diagnostic), Chewy, and Petco to obtain POS/ordered-units data; map ASINs to SKUs; and help drive the transition from sell-in to sell-through model training for accounts where we can get the requisite data
Flag categories or customer accounts where hierarchical demand signals are not being captured by the per-SKU models, and work with the S&OP Manager to identify the right next step
Maintain and improve the forecasting system documentation; ensure any process, template, or engine change is reflected in the project memory so the team can operate efficiently